Hierarchical causal construction for semantic-granularity-aligned counterfactual medical image generation.

Authors

Zhizhong Fu, Wei Zhou, Zhaoyang Jiang, Yudong Lin, Gaoming Li, Yuke Cao, Yifu Hou, Xiaorong Ding, Qiang Yan, Yifan Chen

Published in

Medical image analysis. Volume 116. Pages 104391. Nov 09, 2026. Epub Nov 09, 2026.

Abstract

Counterfactual image generation seeks to answer how a medical image should change when a clinical semantic factor is altered, and is important for model interpretation, lesion localization, and controllable image editing. This work addresses the semantic granularity mismatch in existing counterfactual generation methods and formulates it as the problem of semantic-granularity alignment (SGA). Specifically, counterfactual queries typically target macro-level semantics, such as lesion morphology, tissue intensity, or risk phenotype, whereas generative models operate on micro-level visual units, including pixels, patches, textures, and local boundaries. Without an explicit correspondence between these two levels, macro-level semantic interventions cannot be reliably translated into changes in the relevant local structures. To address this issue, we propose hierarchical causal construction (HCC), which uses causal abstraction to establish a hierarchical mapping between micro-level visual units and macro-level clinical semantics. We further integrate HCC with causal representation learning (CRL) to develop HCC-CRL, which first encodes an image into micro-level causal nodes, and then aggregates them into macro-level causal nodes and semantic slots through a learnable soft quotient mapping. Counterfactual interventions are subsequently performed at the macro level, and the intervened states are projected back into the micro-level visual space for counterfactual image generation. Within a unified hierarchical causal representation, the framework jointly models target semantic changes, preservation of non-target information, and local visual consistency. Experiments on Morpho-MNIST, the Breast Ultrasound Images (BUSI) dataset, and a microvascular invasion (MVI) prediction task show that HCC-CRL generates counterfactual images with well-directed semantic changes and improved structural preservation, while maintaining a robust balance among intervention success, identity preservation, and image quality.

PMID:
43019929
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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